9 results about "Color constancy" patented technology

The invention discloses a semi-supervised non-contact neonatal jaundice home intelligent early warning method based on image calibration, and the method comprises the steps: building a camera response function database, and building a color constancy depth model and a skin region segmentation network; designing a strong and weak enhancement strategy by using a large amount of label-free home data, constructing a time and context comparison learning module, and performing self-supervised training on a feature encoder; a small amount of labeled hospital data and a large amount of unlabeled family data are combined, and semi-supervised fine tuning is realized through pseudo label generation and supervised comparative learning; image sequences continuously uploaded by a user are input into the model, multi-day prediction of the bilirubin level is achieved, prediction uncertainty is quantified in combination with a Bayesian method, the risk boundary is dynamically adjusted, and personalized early warning is provided. According to the method, a user does not need to use a physical colorimetric card, color measurement errors caused by model differences of mobile equipment and variability of household illumination are inhibited from a data source, and the accuracy and robustness of subsequent jaundice assessment are improved; through a training framework combining self-supervised pre-training and semi-supervised fine tuning, the understanding ability and generalization performance of the model for the sequential characteristics of jaundice are improved.

A system, apparatus, and method for processing an image based on color constancy are provided herein. The method comprises the following steps: obtaining an image in a first color space; converting the image into data in a second color space; transforming the data using color adaptation; performing a first normalization on the transformed data, wherein the first normalization includes applying a dynamic spatial filtering technique to adjust the transformed data based on the light intensity; applying a set of filters to the normalized data, wherein the set of filters are convolved based on the normalized data associated with the image; performing a second normalization on the filtered data to obtain an illumination estimate of the image related to the filtered data; and outputting normalized data from the second normalization, where the normalized data maintains color constancy based on the illumination estimate, thereby removing illumination from the normalized data.

This invention discloses a method for detecting the freshness of giant salamander meat based on color constancy and integrated intelligent vision, belonging to the fields of food safety and computer vision technology. The method includes: acquiring RAW images of the meat sample and an indicator film and resolving them into linear radiosity tensors; constructing a color correction model using a latent diffusion local redraw network and a conditional control network; synthesizing a structure-constrained virtual reference color card in a masked region; extracting ambient light source features and performing adaptive white balance compensation to output a standard color image; inputting this image into a deep network optimized by ArcFace to extract a high-dimensional feature vector; and finally inputting this vector into an XGBoost model to fit a nonlinear decision boundary and outputting a freshness rating. This invention effectively reduces color shift interference from complex light sources, improves the recognition accuracy of samples in the critical period of color change, and provides a highly robust solution for non-destructive monitoring of giant salamander meat freshness in actual market environments.

The invention discloses a vertical plastic laying machine for saline-alkali soil sealing and a plastic laying monitoring method based on machine vision, and belongs to the technical field of salt isolating membrane laying. The vertical plastic laying machine for saline-alkali soil sealing comprises a chain type furrow opener, a membrane placing mechanism and an industrial camera; the membrane placing mechanism realizes smooth laying of the isolating membrane through a membrane releasing roller, a guide roller, a vertical roller and a guide plate, and an industrial camera monitors in real time. According to the invention, isolation membrane laying and real-time quality management and control can be realized, and the operation efficiency and the treatment effect are improved. The plastic laying monitoring method based on machine vision comprises the steps that an isolating membrane image in the laying process is collected, and a corrected and enhanced image is obtained through self-adaptive illumination elimination processing; extracting illumination invariant features fusing multi-channel color constancy and gradient magnitude; a multi-scale residual error is calculated, and a defect sensitive feature graph is generated through morphological topological weight modulation; and in combination with real-time tension fluctuation, four states of normality, wrinkling, hemming and damage are identified.

In order to restore the real color of the object in the image in the human eye due to the illumination color of different ambient light changing the color of the object in the image scene, the application discloses a color constancy algorithm based on gray surface extraction, which extracts the gray pixels in the image based on the illumination invariant vector, then uses the gray world algorithm on the pixels to predict the light source in the image, and corrects the color adaptation of the image. According to the color adaptation result, the gray pixels in the image are continuously detected and the color adaptation is carried out through iterative circulation until the set threshold is met, and the algorithm is stopped. Compared with the traditional algorithm, the illumination estimation accuracy is greatly improved.

A cataract classification method based on color invariance and frequency domain attention belongs to the technical field of computer vision and medical artificial intelligence, takes an eye image photographed by a slit lamp as an input data source, constructs a CFC-Mamba classification network integrating a color invariance fusion module and a frequency domain channel attention module, so that the model can effectively extract color robust features which are invariant to devices and enhance the high-frequency detail perception ability to the cataract lesion boundary, adopts color invariance convolution and cross attention fusion mechanism to solve the color offset problem caused by different imaging devices and ensure the cross-device robustness of feature extraction, and through the frequency domain channel attention module, combines Fourier transform, high-pass filtering and double-path channel attention strategy to highlight the high-frequency features of the lesion boundary and improve the fine-grained feature expression ability, and provides a cataract classification system based on color invariance and frequency domain attention. The present application realizes high-precision classification of multi-granularity cataracts.

The invention provides an endoscope image enhancement method and endoscope image enhancement equipment, and relates to the technical field of image processing. According to the invention, the adaptive color constancy loss function calculated based on the first brightness adaptive weight map in the Lab color space is designed, and the adaptive exposure control loss function calculated based on the second brightness adaptive weight map is designed; the image enhancement network is trained based on the self-adaptive color constancy loss function and the self-adaptive exposure control loss function, so that the trained image enhancement network provided by the embodiment of the invention can be based on the to-be-enhanced endoscope image; adaptive learning is carried out to generate a multi-channel curve parameter graph containing a group of pixel-level brightness enhancement curve parameters, and the group of pixel-level brightness enhancement curve parameters corresponding to each pixel point in the multi-channel curve parameter graph is applied to the endoscope image to be enhanced. And the brightness contrast level is maintained while the color fidelity of the enhanced endoscope image to be enhanced is improved.

This invention discloses a method for simultaneous detection of bilirubin and hemoglobin concentrations based on a single-frame image of the eye, comprising: acquiring an image containing a color chart and the eye under a standard light source; performing manual white balance and color correction matrix transformation based on the color chart; extracting the sclera and lower eyelid regions using a YOLOv8 segmentation network; inputting the left and right eye regions into a twin-Swin-Transformer network with shared weights; obtaining predicted values ​​through multi-scale feature fusion and a regression head; and introducing a feature consistency loss optimization model. This invention achieves high-precision simultaneous non-invasive detection of bilirubin and hemoglobin, with advantages such as good color constancy, strong anti-interference ability, and high clinical application value.

The application discloses a color constancy calculation method based on human visual mechanism quantitative modeling, comprising the following steps: based on the spatial local brightness contrast, the original image is recursively propagated to obtain a color correction image based on the spatial local contrast mechanism; scene light source information is estimated by using a maxRGB model, a von Kries diagonal gain matrix is constructed to perform global illumination compensation, and a global color correction image is obtained; a fully connected conditional random field model is constructed, two kinds of correction images are used as prior information of a unary potential function of an energy function, and a global correlation between pixels is modeled by a binary potential function; and finally, a final color correction image is obtained through joint optimization and approximate inference of the energy function. The application realizes unified modeling and relative contribution quantification of color adaptation, spatial local contrast and global light source prediction mechanism, improves the accuracy, interpretability and cross-scene robustness of color constancy calculation, and can be applied to the fields of image enhancement, visual perception and intelligent imaging.